Smoothing parameter selection in Nadaraya-Watson kernel nonparametric regression using nature-inspired algorithm optimization | ||
IRAQI JOURNAL OF STATISTICAL SCIENCES | ||
Article 6, Volume 17, Issue 32, Autumn 2020, Page 62-75 PDF (465 K) | ||
Document Type: Research Paper | ||
DOI: 10.33899/iqjoss.2020.167389 | ||
Authors | ||
Zinah Ameer Basheer ![]() | ||
1Department of Statistics and Informatics, University of Mosul, Mosul, Iraq | ||
2Dept. of Statistics and Informatics/ college of computer sciences and mathematics/ University of Mosul, Mosul, iraq | ||
Abstract | ||
In the context of Nadaraya-Watson kernel nonparametric regression, the curve estimation is fully depending on the smoothing parameter. At this point, the nature-inspired algorithms can be used as an alternative tool to find the optimal selection. In this paper, a firefly optimization algorithm method is proposed to choose the smoothing parameter in Nadaraya-Watson kernel nonparametric regression. The proposed method will efficiently help to find the best smoothing parameter with a high prediction. The proposed method is compared with four famous methods. The experimental results comprehensively demonstrate the superiority of the proposed method in terms of prediction capability. | ||
Keywords | ||
Nadaraya-Watson estimator; firefly optimization algorithm; smoothing parameter selection | ||
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